Cubist::cubist() fits a model that derives simple feature rules from a tree
ensemble and uses creates regression models within each rule.
rules::cubist_fit() is a wrapper around this function.
Details
For this engine, there is a single mode: regression
Tuning Parameters
This model has 3 tuning parameters:
committees: # Committees (type: integer, default: 1L)neighbors: # Nearest Neighbors (type: integer, default: 0L)max_rules: Max. Rules (type: integer, default: NA_integer)
Translation from parsnip to the underlying model call (regression)
The rules extension package is required to fit this model.
library(rules)
cubist_rules(
committees = integer(1),
neighbors = integer(1),
max_rules = integer(1)
) %>%
set_engine("Cubist") %>%
set_mode("regression") %>%
translate()## Cubist Model Specification (regression)
##
## Main Arguments:
## committees = integer(1)
## neighbors = integer(1)
## max_rules = integer(1)
##
## Computational engine: Cubist
##
## Model fit template:
## rules::cubist_fit(x = missing_arg(), y = missing_arg(), weights = missing_arg(),
## committees = integer(1), neighbors = integer(1), max_rules = integer(1))Preprocessing requirements
This engine does not require any special encoding of the predictors.
Categorical predictors can be partitioned into groups of factor levels
(e.g. {a, c} vs {b, d}) when splitting at a node. Dummy variables
are not required for this model.
References
Quinlan R (1992). “Learning with Continuous Classes.” Proceedings of the 5th Australian Joint Conference On Artificial Intelligence, pp. 343-348.
Quinlan R (1993).”Combining Instance-Based and Model-Based Learning.” Proceedings of the Tenth International Conference on Machine Learning, pp. 236-243.
Kuhn M and Johnson K (2013). Applied Predictive Modeling. Springer.